SPEECH RECOGNITION OF ARABIC WORDS USING ARTIFICIAL NEURAL NETWORKS
The speech recognition system has been widely used by many researchers using different methods to fulfill a fast and accurate system. Speech signal recognition is a typical classification problem, which generally includes two main parts: feature extraction and classification. In this paper, a new ap...
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Education for Women
2019-02-01
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| Series: | مجلة كلية التربية للبنات |
| Online Access: | http://jcoeduw.uobaghdad.edu.iq/index.php/journal/article/view/717 |
| Summary: | The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. Twenty three Arabic words were recorded fifteen different times in a studio
by one speaker to form a database. The performance of the proposed system using this
database has been evaluated by computer simulation using MATLAB package. The result
shows recognition accuracy of 65%, 70% and 80% using DWT (Db1), DWT (Db4) and SLT
respectively. |
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| ISSN: | 1680-8738 2663-547X |